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Aug 23, 2024
Using Tableau and Salesforce in practice – Troubleshooting, Tricks, and Best Practices

Now that you have gained a solid understanding of the synergistic power of Salesforce and Tableau, it is time to convert this newfound knowledge into practice. Here are some practical steps to guide you in this exciting journey:

  • Think in terms of business needs: Always frame your application of Salesforce and Tableau in terms of what your business needs. Remember, technology should serve your business goals, not the other way around. Begin by identifying key business questions or challenges, then determine how Salesforce and Tableau can help address them.
  • Start small and scale: It can be tempting to apply your new knowledge across the entire business straight away. However, it is often beneficial to start small. Choose a specific project or team to pilot your initiatives, then gradually scale based on your successes.
  • Practice data visualization: Spend time honing your data visualization skills with Tableau. Data visualization is an art, and just like any art, it improves with practice. Explore different charts and dashboards, play with customization options, and learn what types of visualization best communicate different kinds of information.
  • Dive into Salesforce CRM: Make sure to get your hands dirty with Salesforce’s CRM functionalities. It is one thing to understand it theoretically, but experiencing it firsthand will cement your understanding and help you find ways to enhance your organization’s workflows.
  • Embrace the power of Einstein AI: Do not shy away from the advanced capabilities offered by Einstein AI. While AI might seem intimidating, remember that Einstein AI models require no coding and can greatly boost your analytic capabilities. Do not hesitate to experiment and explore how AI can benefit your organization.
  • Join the community: Salesforce and Tableau have vast, passionate communities full of experts willing to share their experiences and insights. Join forums, participate in webinars, and attend user group meetings. The knowledge and support you can find within these communities is invaluable.
  • Iterate and learn from mistakes: You are likely to face challenges as you begin implementing Salesforce and Tableau. Do not be disheartened. Use these challenges as learning opportunities. Adopt a mindset of continuous improvement, adjusting and refining your approach as you progress.
  • Stay up-to-date: Salesforce and Tableau are dynamic, evolving platforms. Make sure to stay updated with the latest features and improvements. Regularly check official blogs, attend product webinars, and subscribe to newsletters.

Remember, the journey from learning to practice is a marathon, not a sprint. Be patient with yourself, continue learning, and keep refining your skills. With time, you will become proficient in harnessing the combined power of Salesforce and Tableau to drive your organization forward.

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Jun 13, 2024
Introduction– Troubleshooting, Tricks, and Best Practices

This chapter will summarize why Tableau and Salesforce are such a powerful combination, with an emphasis on their capabilities for data analysis, visualization, and decision-making. Additionally, the chapter will provide tips on how to get started using Tableau and Salesforce in practice, with guidance on best practices and useful resources. It will also contain guidance on troubleshooting common issues. The chapter will also explain how to continue the learning journey, including recommended further reading. Lastly, the chapter will help you start using the knowledge gained to improve your data analysis and decision making skills.

Structure

The chapter covers the following topics:

  • Tableau and Salesforce: a powerful combination
  • Trouble shooting guidance
  • Troubleshooting guidance
  • Continuing the learning journey

Objectives

This chapter is designed to provide learners with a thorough understanding of the combined use of Tableau and Salesforce for data analysis and visualization, highlighting their unique strengths and synergies. It delves into the reasons why integrating Tableau with Salesforce can significantly enhance business decision-making capabilities.

Learners will also be equipped with essential tips and best practices for effectively starting with Tableau and Salesforce in practical, real-world scenarios. Furthermore, the chapter includes strategies for troubleshooting common issues that may arise in the Tableau and Salesforce environments.

It also guides learners to discover a range of useful resources, tools, and platforms that can support their journey in mastering these technologies. The importance of continuous learning is emphasized, with strategies provided for staying current with the latest trends and developments in Tableau and Salesforce.

Additionally, learners will find recommendations for further reading that can deepen their understanding and proficiency in using these tools. Finally, the chapter aims to encourage and empower learners to confidently apply the knowledge and skills acquired from this book in their data analysis and decision-making tasks.

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Apr 5, 2024
Combining Salesforce and Tableau for Advanced Analytics– Exploring Einstein AI and Advanced Analytics-1

To give an example of how you might combine Tableau and Salesforce to achieve advanced analytical use cases, we will bring the Einstein Discovery prediction model that you created in Chapter 8, Blending Tableau with Traditional CRM Analytics into Tableau Desktop. If you have not already completed the setup of this Einstein Discovery model, now is the time to do so.
We will employ the Tableau Einstein Discovery connector to bring the predictions into our Tableau environment, and thereby we will learn one of the most important ways of combining CRM Analytics with Tableau Desktop to get more out of both.
To start with, let us get the connection set up. Please follow the steps below to set up the connection between Tableau Desktop and Einstein Discovery:

  1. Go to your model from Chapter 8 in Analytics Studio. It can be reached via Browse |Models, as shown in Figure 9.1:

Figure 9.1: Deploy model screen in Analytics Studio

  1. Now open your model and deploy it by clicking Deploy Model; this will make it usable from Tableau Desktop, among other places. You can see this in the Figure 9.2:

Figure 9.2: Model deployment options

  1. Leave the defaults as is and ignore any model warnings; they are not relevant to the example. See the warning in the Figure 9.3:

Figure 9.3: Ignore warnings

  1. On the following screen, select Deploy without connecting to a Salesforce object, as we are not writing back to the CRM but using it from Tableau. You can see this in Figure 9.4:

Figure 9.4: Deployment settings

  1. We will not be segmenting our data, although it is worth noting this capability for future investigation, so select Don’t segment, as shown in Figure 9.5:

Figure 9.5: Segmentation options

  1. Under Actionable variables, indicated in Figure 9.6, select Lead Source and Industry” as these are within our gift to influence, although not fully control.

Figure 9.6: Actionable variables

  1. At this point, we will not customize our predictions. Therefore, select Don’t customize, as shown below.

Figure 9.7: Customization options

  1. Click Deploy to deploy the model, as shown in Figure 9.8:

Figure 9.8: Deploy model

  1. When you are redirected to a screen as shown Figure 9.9, you are ready to proceed to the next stage.

Figure 9.9: Model deployed

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Jan 30, 2024
Deeper dive in Einstein discovery – Exploring Einstein AI and Advanced Analytics

Salesforce: Advanced analytics capabilities

The following sections will dive into the advanced analytics capabilities within Salesforce and CRMA. This will teach you the options for working with advanced analytics within Salesforce prior to diving into the hands-on case.

Deeper dive in Einstein discovery

Einstein Discovery is a powerful tool within the context of advanced analytics. It can seamlessly incorporate Artificial Intelligence (AI) and machine learning into a business user’s workflow, allowing for the creation of predictive models without the need for data scientists or advanced developers. To truly leverage this platform’s potential, users must understand their data, interpret model outcomes, and correctly configure training datasets.

Advanced analytics involves sophisticated techniques and tools that delve beyond the traditional realm of BI to uncover deeper insights, make predictions, and offer recommendations. Einstein Discovery aligns with several aspects of advanced analytics, such as machine learning, pattern matching, and forecasting. Utilizing a no-code AI approach, the platform embeds algorithms to train its models, eliminating the need for manual coding.

Choosing the right features and incorporating business knowledge is an important process in utilizing Einstein Discovery effectively. Business representatives and data analysts need to collaborate from the start of the model development process. This collaboration ensures that the most relevant predictors are chosen and that the model’s outputs are aligned with business needs and goals.

Some key practices for model enhancement in Einstein Discovery include segmenting datasets into different models, handling multicollinearity, managing diverse inputs, detecting and handling outliers, and choosing the optimal bucketing method for every use case. It is essential to balance the accuracy that the model delivers with the need for understanding why the prediction was made, with business requirements often guiding these decisions.

Segmentation can improve prediction quality by creating separate models for different data segments, accommodating the behavior of each segment in the model. Tests such as examining distributions of input variables can be used to determine when segmentation is a viable strategy.

Multicollinearity occurs when dependent variables can be linearly predicted from each other, causing potential issues in model quality. Methods to address multicollinearity include a thorough investigation of data, understanding business relations between variables, and using Einstein Discovery to highlight problematic relationships.

Outliers can impact the accuracy and explainability of models. Detecting and managing outliers can improve model quality by reducing noise. Einstein Discovery can recommend an approach for handling outliers, but business input is crucial in the decision-making process.

Bucketing is an essential consideration in model quality and explainability. The right balance between optimal model metrics and clear explanations can be achieved by using different bucketing methods such as bucket by count, manual bucketing, bucket by width, or Einstein Discovery’s recommended buckets.

Lastly, adding explicit second-order variables representing interactions between different features can also improve model accuracy. However, this method should be used with caution, taking into account potential limitations such as the number of possible column value combinations and the variety of data reflecting those combinations.

Einstein Discovery provides a powerful means of advanced analytics by incorporating machine learning, pattern matching, and forecasting techniques seamlessly into the business user’s workflow. Close collaboration between business users and data analysts ensures that the right features are chosen and that the model is both accurate and relevant to the company’s goals. Adopting various model enhancement practices, such as strategically segmenting datasets and fine-tuning bucketing methods, can help perfect the insights provided by Einstein Discovery and maximize its value as an advanced analytics solution.

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Dec 24, 2023
Tableau: Advanced analytics capabilities – Exploring Einstein AI and Advanced Analytics

Tableau and Salesforce provide powerful analytics capabilities that cater to both business users and data scientists. This comprehensive platform offers a wide range of features, including segmentation and cohort analysis, what-if and scenario analysis, sophisticated calculations, time-series and predictive analysis, and external services integration. By combining advanced analytics features with an intuitive interface, Tableau enables users to perform complex analyses and gain valuable insights from their data quickly and efficiently. Here are the applications:

  • Segmentation and cohort analysis Tableau enables quick, iterative analysis and comparison of segments to generate initial hypotheses and validate them. This section covers various capabilities in Tableau, such as:
  • Clustering: Tableau’s clustering feature uses unsupervised machine learning to segment data based on multiple variables.
  • Sets and set actions: Sets in Tableau allow defining collections of data objects either by manual selection or programmatic logic. Set Actions enable storing a selection of data points within a set, allowing for use cases like proportional brushing.
  • Groups: Groups in Tableau support creating ad-hoc categories and establishing hierarchies, which can help with basic data cleaning needs and structuring data intuitively for analysis tasks.
  • What-if and scenario analysis: Tableau enables users to experiment with inputs of their analysis and share scenarios while keeping data fresh using parameters and story points.
  • Parameters: Parameters in Tableau allow changing input values into a model or dashboard, driving calculations, altering filter thresholds, and selecting data.
  • Story points: Story points in Tableau enable constructing presentations that update with data changes and retain parameter values.
  • Sophisticated calculations: Tableau offers powerful capabilities to support complex logic using calculated fields. Two types of calculated fields that enable advanced analysis are:
    • Level of detail expressions: LOD Expressions in Tableau are an extension of the calculation language, enabling answering questions involving multiple levels of granularity in a single visualization.
    • Table calculations: Table calculations in Tableau enable relative computations applied to all values in a table and are often dependent on the table structure itself.
  • Time-series and predictive analysis: Tableau simplifies time-series analysis and offers predictive capabilities such as trending and forecasting.
  • Time-series analysis: Tableau’s flexible front end and powerful back end make time-series analysis a simple matter of asking the right questions.
  • Forecasting: Tableau’s forecasting functionality runs several different models in the background and selects the best one, automatically accounting for data issues such as seasonality.
  • External services integration: Tableau integrates with external services like Python, R, and MATLAB to expand functionality and leverage existing investments in other solutions.
  • Python, R, and MATLAB integrations: Tableau integrates directly with Python, R, and MATLAB, allowing users to call any function available in R or Python on data in Tableau and manipulate models created in these environments using Tableau.

In conclusion, Tableau’s advanced analytics capabilities make it a versatile and valuable tool for users at all levels of expertise. By offering a wide range of features, such as clustering, calculated fields, forecasting, and integration with Python, R, and MATLAB, Tableau empowers users to perform complex analyses and derive actionable insights from their data.

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Aug 17, 2023
Conclusion – Blending Tableau with Traditional CRM Analytics

In this chapter, we have journeyed through the crucial domain of CRM Analytics and its paramount role in understanding, managing, and improving customer relationships. You should now be able to grasp the full picture of CRM Analytics, its key features, and how it facilitates thorough analysis of customer data through datasets and lenses.

The concept of Einstein Discovery within Salesforce, and its application in the CRM Analytics platform, was another essential part of our journey. Now, you should be capable of creating and utilizing Einstein Discovery models to enhance your data interpretation and decision-making processes.

Finally, we discussed the use of the CRM Analytics Tableau Output Connector, a bridge that allows Salesforce data to flow seamlessly into Tableau. This key tool enables you to perform detailed, insightful analysis of your Salesforce data, further enriching your understanding and enabling you to derive practical benefits.

In sum, we have armed you with a comprehensive understanding of CRM Analytics within Salesforce, its integration with Tableau, and the immense value these tools bring to your organization. Now, you should feel confident to apply these skills in your own work, harnessing the power of CRM Analytics and Tableau to transform raw data into strategic actions, and thereby improve your business operations and customer relationships.

We will now move on to putting our hard work on setting up CRMA to use inside Tableau for advanced analytics use cases, while also covering the basics of what advanced analytics are really all about in the process.

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Jul 5, 2023
Using the Tableau online output connection – Blending Tableau with Traditional CRM Analytics

The Tableau Online Output Connector is a powerful tool that enables you to seamlessly push your prepared data from CRM Analytics into Tableau Online for further analysis. By transforming, merging, and cleaning your data in CRM Analytics, you can easily create a .hyper file that can be analyzed using Tableau Online’s advanced analytics tools. This connector is designed to work with Data Prep recipes and requires a Creator license for the Tableau Online account. In this section, we will walk you through the process of enabling the Tableau Online Output Connector, configuring connection settings, and pushing data to Tableau Online.
In this example, we will write the opportunity_history dataset that we have been using all through this chapter to our Tableau Cloud environment, using the Tableau Online Output Connector.
To do so follow the instructions below:

  1. First, go to setup and search for analytics in the Quick Find box. Click Settings.
  2. Under settings, shown in the following screenshot, enable the Tableau Online output connection and save:

Figure 8.39: Analytics settings showing Tableau connector

  1. Go to Analytics Studio and click Data Manager, then from the page that appears, click Connections. This should look like the following screenshot:

Figure 8.40: Data manager showing connections

  1. Click New Connection, select Output under Connector Type and click Tableau Online Output Connector, as shown below:

Figure 8.41: New connection dialogue

  1. Now fill in the information as in the screenshot below. You can refer to this URL for the details of the parameters if needed: https://help.salesforce.com/s/articleView?id=sf.bi_integrate_connectors_output_tableau_hyper.htm&type=5

Figure 8.42: Tableau Online connector configuration

  1. Now you have established the connection. Time to test it. To do so, go back to Data Manager and click on Recipes, as shown in the following screenshot:

Figure 8.43: Data manager recipes tab

  1. On the Canvas that appears, click Add Input Data, shown in the following screenshot:

Figure 8.44: Add input data button

  1. Select the opportunity_history dataset, as shown in the following figure:

Figure 8.45: opportunity_history dataset selected as input

  1. Now add a node by clicking on “+”. Select Output, shown below:

Figure 8.46: Output node being added

  1. Fill out the form as per the screenshot below:

Figure 8.47: Output node configuration

  1. Save and Run the Recipe by clicking on the button and giving it a name, for instance as in the following screenshot:

Figure 8.48: Save and run recipe dialogue

  1. The job will now run, so wait until it has been completed. You can monitor this in Jobs Monitor, shown in Figure 8.49:

Figure 8.49: Jobs monitor showing recipe run
When the job is complete, you can find and explore the dataset in Tableau Cloud by clicking Explore, selecting default, and then clicking on Extract, which is the extract you have just created. This will look as in the following screenshot:

Figure 8.50: Explore dataset in Tableau dialogue
We have now covered the material for this chapter. Well done! However, we have even further heights to scale as we explore the world of advanced analytics in Chapter 9 – Exploring Einstein AI and Advanced Analytics.

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Jan 7, 2023
Building a lens – Blending Tableau with Traditional CRM Analytics

A lens is a visual representation of the data within a dataset, allowing you to explore data graphically and construct queries for a dashboard. To build a lens, follow the steps below:

  1. Click on the Analytics Studio tab to return to the CRM Analytics Home page.
  2. Click Browse and then select Datasets.
  3. Choose the opportunity_history dataset, which will open a new tab, shown below, with a lens for exploring the dataset.

Figure 8.10: Opportunity_history dataset details

  1. In the New Lens tab, click on Count of Rows located beneath Bar Length, as shown in the following screenshot:

Figure 8.11: Lens builder with Count of Rows highlighted

  1. Select Sum and then choose Amount from the dropdown menu, as shown in the next screenshot:

Figure 8.12: Sum Amount selected

  1. Under Bars, click the plus sign (+) and select Industry, as shown below:

Figure 8.13: Industry field added to lens

  1. Click the plus sign (+) under Bars again, and choose Opportunity Type, as shown below:

Figure 8.14: Opportunity Type added to lens

  1. Under Bar Length, click the arrow next to Sum of Amount and select Sort Descending, shown in Figure 8.15:

Figure 8.15: Sort descending selected

  1. Click the Charts icon to access different chart options, shown in the following screenshot:

Figure 8.16: Charts icon highlighted

  1. Select the Stacked Column chart icon, shown in the screenshot below, to create a Stacked Column chart, which will display the sum of the amount according to the industry.

Figure 8.17: Stacked column chart selected

  1. Save your lens by clicking the Save button.
  2. Enter My Test Lens, indicated in the following screenshot, as the title of your new lens, and then choose App | My Test App from the dropdown menu.

Figure 8.18: Save lens dialogue

  1. Click Save to complete the process.
    Your final product should look like the following:

Figure 8.19: Completed stacked column lens visualization
With your app, dataset, and lens now created, you can proceed to create a dashboard.

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Nov 8, 2022
Creating an analytics app – Blending Tableau with Traditional CRM Analytics

A CRM Analytics app is a comprehensive collection of analyses, data exploration paths, and powerful tools designed for in-depth, real-time data examination. CRM Analytics relies on apps to organize data projects, run presentations directly from dashboards, and manage asset sharing.
To get started with creating an app in your CRM Analytics-enabled Developer Edition org, follow these steps:

  1. Open your CRMA Developer Edition org.
  2. Access the App Launcher and search for Analytics Studio. Select it to open Analytics Studio in a new tab. Keep both tabs open, as you will need to work on the original tab later in the project. This is shown in the following screenshot:

Figure 8.1: App Launcher showing Analytics Studio

  1. In Analytics Studio, click the Create button and select App from the dropdown menu, as shown below:

Figure 8.2: Create menu dropdown showing App option

  1. Choose Create Blank App to start with a clean slate, as shown in the following screenshot:

Figure 8.3: Blank App creation dialogue

  1. Click Continue to proceed to the next step. Enter My Test App as the name of your new app, as shown below:

Figure 8.4: My Test App creation dialogue

  1. Click Create to finalize the app creation process.
    Congratulations! You have successfully created an app in CRM Analytics. We will now move on to importing a dataset.
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Aug 7, 2022
Conclusion-Integration, Authentication, and Tableau Viz LWC

It is important to note that Tableau-connected apps and Salesforce-connected apps are different and offer distinct functionalities. Currently, Tableau connected apps are intended for embedding Tableau views and metrics in external applications and authorizing access to the Tableau REST API.

Generally, you should not use a Connected App with Salesforce. The one exception might be if you are planning to use the same app across several target systems that need to embed information from Tableau and should be managed in a consistent way. You would just be creating more trouble for yourself.

Finally, there is the option of embedding Tableau Dashboard as a canvas app using the Sparkler framework. This used to be the preferred way of embedding Tableau Dashboards into Salesforce, but it has now been superseded by the Tableau Viz LWC Component. The setup for this option is very complex. It involves a Java-based application, Sparkler, which can be used to embed Tableau dashboards in Salesforce using Salesforce’s canvas framework.

To set up Sparkler, you must download the adapter, create a virtual machine to run it, install Java 8, install Tomcat, enable HTTPS for Tomcat, install Sparkler, configure secure communication between Sparkler and Tableau Online, and configure a connection between Salesforce and Sparkler.

Finally, you must embed and filter the dashboard on a record in Lightning Experience by creating a new Visualforce page and customizing the record page. All in all, not something you want to do, given other options. However, you should know it as you could see it in a legacy environment.

Conclusion

In this chapter, we have provided a comprehensive guide on integrating Tableau with Salesforce using the Tableau Viz LWC component. You have learned the purpose and benefits of this integration, as well as the process of installing and configuring the Tableau Viz LWC component for seamless integration in Salesforce.

Furthermore, we have delved into advanced usage techniques, including the creation of custom visualizations and modifying the component’s settings. We have also covered the implementation of Single Sign-On (SSO) between Salesforce and Tableau to streamline the authentication process and enhance security.

Additionally, we have explored alternative methods for connecting Salesforce and Tableau dashboards, such as direct connections and third-party integration tools. With the knowledge gained from this chapter, you are now well-equipped to enhance your CRM analytics in Salesforce using the Tableau Viz LWC component and make more data-driven decisions to drive business success.

In the next chapter, we will dive deeper into how you can combine Tableau with CRM Analytics to create game-changing analytical use cases.

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